# i is for removing at least
# j is for aggregation level
# k is for clustersize
#i=2 ; j = 9, k = 10
#dat_tax_aux <- aggregating_compositions(
# dFrame = dat_tax,
# fillZeros = 'Nothing',
# aggregating_level = vec_aggregation[1],
# PresentAtLeast = 1,
# metadata = metadat
#) %>% mutate(
# OBS=1:n()) %>%
# select(OBS,Latitude,Depth,Pressure_decibars,
# Salinity_psu,Temperature_degrees_Celsius)
#
#outputSS <- list()
#kmax = length(listW2$AtLeast1$PG)
#for (i in 1:3){
#
# outputAgg <- list()
# for(j in 1:length(vec_aggregation)){
#
# outputDistances <- list()
# for(k in 1:kmax){
#
# dat_tax_aux =
# dat_tax_aux %>% mutate(
# Ward_Clust = factor(listW2[[i]][[j]][[k]]),
# Medoid_Clust = factor(listMedois[[i]][[j]][[k]]))
#
# outputDistances[[k]] <- list(
# Ward=dat_tax_aux %>% geoCoherense(ClustVar = 'Ward_Clust',DephtVar = 'DepthRank'),
# Medoid=dat_tax_aux %>% geoCoherense(ClustVar = 'Medoid_Clust',DephtVar = 'DepthRank'))
# }
# names(outputDistances) <- ifelse(1:kmax < 10,
# paste('Cluster0',1:kmax,sep=''),
# paste('Cluster',1:kmax,sep=''))
# outputAgg[[j]]<-outputDistances
# rm(outputDistances)
# }
#
# names(outputAgg) <- vec_aggregation
# outputSS[[i]] <- outputAgg
# rm(outputAgg)
#}
#
#names(outputSS) <- paste('AtLeastIn',1:3,sep='')
#
#saveRDS(object = outputSS,file = 'outputSS')
#outputSS=readRDS('outputSS')
#
#df_outputSS <- outputSS %>% plyr::ldply(function(atleast){
# atleast %>% plyr::ldply(function(agglevel){
# agglevel %>% plyr::ldply(function(cluster){
# cluster %>% plyr::ldply(function(method){
# method %>% plyr::ldply(function(dimension){
# return(data.frame(TotalSum = sum(dimension$SumDist)))
# }, .id = "dimension")
# }, .id = "method")
# }, .id = "clusters")
# }, .id = "agglevel")
#}, .id = "atleast")
#
#data.table::fwrite(df_outputSS,'/Users/rafaelcatoia/Desktop/repos/Capstone/df_outputSS.csv')
df_outputSS <- data.table::fread('https://raw.githubusercontent.com/rafaelcatoia/zoop_16N/main/df_outputSS.csv')
#######--- Doing the same thing now, but wit the rank instead of the true Depht
# dat_tax_aux <- aggregating_compositions(
# dFrame = dat_tax,
# fillZeros = 'Nothing',
# aggregating_level = vec_aggregation[1],
# PresentAtLeast = 1,
# metadata = metadat
# ) %>% mutate(
# OBS=1:n()) %>%
# select(OBS,Latitude,Depth,Pressure_decibars,
# Salinity_psu,Temperature_degrees_Celsius) %>%
# left_join(
# metadat %>% select(Samples,Latitude,Depth) %>%
# arrange(Samples,Depth) %>%
# group_by(Latitude) %>% mutate(DepthRank=1:n()))
#
# outputSS_Rank <- list()
# kmax = length(listW2$AtLeast1$PG)
# for (i in 1:3){
#
# outputAgg <- list()
# for(j in 1:length(vec_aggregation)){
#
# outputDistances <- list()
# for(k in 1:kmax){
#
# dat_tax_aux =
# dat_tax_aux %>% mutate(
# Ward_Clust = factor(listW2[[i]][[j]][[k]]),
# Medoid_Clust = factor(listMedois[[i]][[j]][[k]]))
#
# outputDistances[[k]] <- list(
# Ward=dat_tax_aux %>% geoCoherense(ClustVar = 'Ward_Clust',DephtVar = 'DepthRank'),
# Medoid=dat_tax_aux %>% geoCoherense(ClustVar = 'Medoid_Clust',DephtVar = 'DepthRank'))
# }
# names(outputDistances) <- ifelse(1:kmax < 10,
# paste('Cluster0',1:kmax,sep=''),
# paste('Cluster',1:kmax,sep=''))
# outputAgg[[j]]<-outputDistances
# rm(outputDistances)
# }
#
# names(outputAgg) <- vec_aggregation
# outputSS_Rank[[i]] <- outputAgg
# rm(outputAgg)
# }
#
# names(outputSS_Rank) <- paste('AtLeastIn',1:3,sep='')
#
# saveRDS(object = outputSS_Rank,file = 'outputSS_Rank')
# outputSS_Rank=readRDS('outputSS_Rank')
#
# df_outputSS_Rank <- outputSS_Rank %>% plyr::ldply(function(atleast){
# atleast %>% plyr::ldply(function(agglevel){
# agglevel %>% plyr::ldply(function(cluster){
# cluster %>% plyr::ldply(function(method){
# method %>% plyr::ldply(function(dimension){
# return(data.frame(TotalSum = sum(dimension$SumDist)))
# }, .id = "dimension")
# }, .id = "method")
# }, .id = "clusters")
# }, .id = "agglevel")
# }, .id = "atleast")
#data.table::fwrite(df_outputSS_Rank,'/Users/rafaelcatoia/Desktop/repos/Capstone/df_outputSS_Rank.csv')
df_outputSS_Rank <- data.table::fread('https://raw.githubusercontent.com/rafaelcatoia/zoop_16N/main/df_outputSS_Rank.csv')